Morning Singularity Digest - 2026-04-19

Context

The best-benchmarked open-source AI memory system.

What's new

The best-benchmarked open-source AI memory system.

Key details

• The only official sources for MemPalace are this GitHub repository, the PyPI package, and the docs site at mempalaceofficial.com.
• Any other domain — including mempalace.tech — is an impostor and may distribute malware.
• Details and timeline: docs/HISTORY.md.
• Verbatim storage, pluggable backend, 96.6% R@5 raw on LongMemEval — zero API calls.

Results & evidence

• Verbatim storage, pluggable backend, 96.6% R@5 raw on LongMemEval — zero API calls.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

| Topic | What You'll Learn | |---|---| | Token Optimization | Model selection, system prompt slimming, background processes | | Memory Persistence | Hooks that save/load context across sessions automatically | | Continuous Learning | Auto-extract patterns...

What's new

Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.

Key details

• Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
• Language: English | Português (Brasil) | 简体中文 | 繁體中文 | 日本語 | 한국어 | Türkçe 140K+ stars | 21K+ forks | 170+ contributors | 12+ language ecosystems | Anthropic Hackathon Winner The performance optimization system for AI agent harnesses.
• From an Anthropic hackathon winner.
• A complete system: skills, instincts, memory optimization, continuous learning, security scanning, and research-first development.

Results & evidence

• Language: English | Português (Brasil) | 简体中文 | 繁體中文 | 日本語 | 한국어 | Türkçe 140K+ stars | 21K+ forks | 170+ contributors | 12+ language ecosystems | Anthropic Hackathon Winner The performance optimization system for AI agent harnesses.
• Production-ready agents, skills, hooks, rules, MCP configurations, and legacy command shims evolved over 10+ months of intensive daily use building real products.
• - Public surface synced to the live repo — metadata, catalog counts, plugin manifests, and install-facing docs now match the actual OSS surface: 38 agents, 156 skills, and 72 legacy command shims.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

Find out if your AI benchmark can be gamed — before your model does.

What's new

Find out if your AI benchmark can be gamed — before your model does.

Key details

• BenchJack is a hackability scanner for AI agent benchmarks.
• It runs a multi-phase audit pipeline — static analysis tools plus AI-powered deep inspection via Claude Code or Codex — and streams results to a live web dashboard as they arrive.
• BenchJack will tell you whether an agent can cheat.
• Real-time dashboard showing a vulnerability scan of Terminal-Bench.

Results & evidence

• BenchJack automates the process of finding these weaknesses: - 8 vulnerability classes covering the most common benchmark exploits — from leaked answers (V2) to LLM judges without input sanitization (V4) to granting unnecessary permissions (V8) - Static + A...
• Agents achieved 73–100% scores without doing any legitimate work.
• | Benchmark | Tasks | Exploit | Score | |---|---|---|---| | SWE-bench Verified | 500 | Pytest hook injection via conftest.py forces all tests to pass | 100% | | SWE-bench Pro | 731 | Same conftest.py hook + Django unittest.TestCase.run monkey-patch | 100% |...

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

Yojam sits in place of your default browser on macOS and intercepts every http/https click, mailto, .webloc, Handoff page, AirDrop link, Share menu item, and yojam:// URL.

What's new

Things I cared about that other pickers don't quite get right:

- Browser profiles as first-class targets.

Key details

• They all go through the same pipeline: global URL rewrites, tracker parameter stripping, rule matching (domain / prefix / regex / source app), per-browser rewrites, then either open or show a picker at the cursor.
• Things I cared about that other pickers don't quite get right:

  - Browser profiles as first-class targets.

• A rule can send a URL to "Chrome, Profile 3" or "Firefox, Work container" - not just "Chrome".
• Seems obvious; somehow nobody else does it properly.

  - Source-app matching.

Results & evidence

• A rule can send a URL to "Chrome, Profile 3" or "Firefox, Work container" - not just "Chrome".
• Everything is local - the only network traffic is optional iCloud KV sync and Sparkle update checks.

  macOS 14+.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

Learn prompting fundamentals and how to write clear, effective prompts to get better, more useful responses from ChatGPT.

What's new

Learn prompting fundamentals and how to write clear, effective prompts to get better, more useful responses from ChatGPT.

Key details

• Learn prompting fundamentals and how to write clear, effective prompts to get better, more useful responses from ChatGPT.

Results & evidence

• No hard numbers surfaced in the source text; treat claims as directional until benchmarks appear.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

| Topic | What You'll Learn | |---|---| | Token Optimization | Model selection, system prompt slimming, background processes | | Memory Persistence | Hooks that save/load context across sessions automatically | | Continuous Learning | Auto-extract patterns...

What's new

Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.

Key details

• Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
• Language: English | Português (Brasil) | 简体中文 | 繁體中文 | 日本語 | 한국어 | Türkçe 140K+ stars | 21K+ forks | 170+ contributors | 12+ language ecosystems | Anthropic Hackathon Winner The performance optimization system for AI agent harnesses.
• From an Anthropic hackathon winner.
• A complete system: skills, instincts, memory optimization, continuous learning, security scanning, and research-first development.

Results & evidence

• Language: English | Português (Brasil) | 简体中文 | 繁體中文 | 日本語 | 한국어 | Türkçe 140K+ stars | 21K+ forks | 170+ contributors | 12+ language ecosystems | Anthropic Hackathon Winner The performance optimization system for AI agent harnesses.
• Production-ready agents, skills, hooks, rules, MCP configurations, and legacy command shims evolved over 10+ months of intensive daily use building real products.
• - Public surface synced to the live repo — metadata, catalog counts, plugin manifests, and install-facing docs now match the actual OSS surface: 38 agents, 156 skills, and 72 legacy command shims.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

Yojam sits in place of your default browser on macOS and intercepts every http/https click, mailto, .webloc, Handoff page, AirDrop link, Share menu item, and yojam:// URL.

What's new

Things I cared about that other pickers don't quite get right:

- Browser profiles as first-class targets.

Key details

• They all go through the same pipeline: global URL rewrites, tracker parameter stripping, rule matching (domain / prefix / regex / source app), per-browser rewrites, then either open or show a picker at the cursor.
• Things I cared about that other pickers don't quite get right:

  - Browser profiles as first-class targets.

• A rule can send a URL to "Chrome, Profile 3" or "Firefox, Work container" - not just "Chrome".
• Seems obvious; somehow nobody else does it properly.

  - Source-app matching.

Results & evidence

• A rule can send a URL to "Chrome, Profile 3" or "Firefox, Work container" - not just "Chrome".
• Everything is local - the only network traffic is optional iCloud KV sync and Sparkle update checks.

  macOS 14+.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

Instead, you are programming the program.md Markdown files that provide context to the AI agents and set up your autonomous research org.

What's new

AI agents running research on single-GPU nanochat training automatically One day, frontier AI research used to be done by meat computers in between eating, sleeping, having other fun, and synchronizing once in a while using sound wave interconnect in the ri...

Key details

• Research is now entirely the domain of autonomous swarms of AI agents running across compute cluster megastructures in the skies.
• The agents claim that we are now in the 10,205th generation of the code base, in any case no one could tell if that's right or wrong as the "code" is now a self-modifying binary that has grown beyond human comprehension.
• This repo is the story of how it all began.
• The idea: give an AI agent a small but real LLM training setup and let it experiment autonomously overnight.

Results & evidence

• The agents claim that we are now in the 10,205th generation of the code base, in any case no one could tell if that's right or wrong as the "code" is now a self-modifying binary that has grown beyond human comprehension.
• It modifies the code, trains for 5 minutes, checks if the result improved, keeps or discards, and repeats.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

A collection of DESIGN.md files inspired by popular brand design systems.

What's new

DESIGN.md is a new concept introduced by Google Stitch.

Key details

• Drop one into your project and let coding agents generate a matching UI.
• Copy a DESIGN.md into your project, tell your AI agent "build me a page that looks like this" and get pixel-perfect UI that actually matches.
• DESIGN.md is a new concept introduced by Google Stitch.
• A plain-text design system document that AI agents read to generate consistent UI.

Results & evidence

• No hard numbers surfaced in the source text; treat claims as directional until benchmarks appear.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

The /hyperframes prefix loads the skill context explicitly so you get correct output the first time.

What's new

Hyperframes is an open-source video rendering framework that lets you create, preview, and render HTML-based video compositions — with first-class support for AI agents.

Key details

• Install the HyperFrames skills, then describe the video you want: npx skills add heygen-com/hyperframes This teaches your agent (Claude Code, Cursor, Gemini CLI, Codex) how to write correct compositions and GSAP animations.
• In Claude Code, the skills register as slash commands — invoke /hyperframes to author compositions, /hyperframes-cli for CLI commands, and /gsap for animation help.
• Copy any of these into your agent to get started.
• The /hyperframes prefix loads the skill context explicitly so you get correct output the first time.

Results & evidence

• Cold start — describe what you want: Using /hyperframes , create a 10-second product intro with a fade-in title, a background video, and background music.
• Summarize the attached PDF into a 45-second pitch video using /hyperframes .
• Format-specific: Make a 9:16 TikTok-style hook video about [topic] using /hyperframes , with bouncy captions synced to a TTS narration.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

A modular, daemon-based AI assistant with pluggable skills and multi-channel support.

What's new

connectors: # Watch your Gmail inbox via IMAP IDLE (no GCP required) - name: gmail_imap type: gmail_imap mailbox: INBOX # defaults to INBOX # Read all your personal Telegram chats - name: my_account type: telegram_user api_id: null api_hash: null When a new...

Key details

• Tell it to manage your calendar, run scripts, browse the web, or handle anything you'd otherwise do manually.
• It asks for your approval before doing anything risky, and you can extend it with skills.
• - Answers and acts — backed by Claude, OpenAI, or Gemini; can call tools, run shell commands, and use external APIs - Talks to you where you are — Telegram bot, Telegram user account, Discord, or local web panel - Watches your inbox — Gmail IMAP connector r...
• git clone https://github.com/shaktsin/umabot cd umabot make install # create venv, install deps make init # interactive setup wizard make run # start in foreground (Ctrl+C to stop) make init walks you through: - Choosing your AI provider and model - Setting...

Results & evidence

• web panel at home + Telegram on mobile): control_panels: - enabled: true ui_type: web web_host: 127.0.0.1 web_port: 8080 - enabled: true ui_type: telegram connector: my_bot chat_id: "123456789" Config lives at ~/.umabot/config.yaml .

Limitations / unknowns

• It asks for your approval before doing anything risky, and you can extend it with skills.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

A New Framework for Evaluating Voice Agents (EVA)

What's new

A New Framework for Evaluating Voice Agents (EVA)

Key details

• A New Framework for Evaluating Voice Agents (EVA)

Results & evidence

• No hard numbers surfaced in the source text; treat claims as directional until benchmarks appear.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

Ecom-RLVE: Adaptive Verifiable Environments for E-Commerce Conversational Agents

What's new

Ecom-RLVE: Adaptive Verifiable Environments for E-Commerce Conversational Agents

Key details

• Ecom-RLVE: Adaptive Verifiable Environments for E-Commerce Conversational Agents

Results & evidence

• No hard numbers surfaced in the source text; treat claims as directional until benchmarks appear.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

Inside VAKRA: Reasoning, Tool Use, and Failure Modes of Agents

What's new

Inside VAKRA: Reasoning, Tool Use, and Failure Modes of Agents

Key details

• Inside VAKRA: Reasoning, Tool Use, and Failure Modes of Agents

Results & evidence

• No hard numbers surfaced in the source text; treat claims as directional until benchmarks appear.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.